Overview

Dataset statistics

Number of variables30
Number of observations16810
Missing cells50812
Missing cells (%)10.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.8 MiB
Average record size in memory240.0 B

Variable types

Categorical7
Numeric23

Warnings

date has a high cardinality: 3499 distinct values High cardinality
team1 has a high cardinality: 101 distinct values High cardinality
team2 has a high cardinality: 108 distinct values High cardinality
qb1 has a high cardinality: 625 distinct values High cardinality
qb2 has a high cardinality: 642 distinct values High cardinality
elo1_pre is highly correlated with elo1_post and 2 other fieldsHigh correlation
elo2_pre is highly correlated with elo2_post and 2 other fieldsHigh correlation
elo_prob1 is highly correlated with elo_prob2 and 2 other fieldsHigh correlation
elo_prob2 is highly correlated with elo_prob1 and 2 other fieldsHigh correlation
elo1_post is highly correlated with elo1_pre and 2 other fieldsHigh correlation
elo2_post is highly correlated with elo2_pre and 2 other fieldsHigh correlation
qbelo1_pre is highly correlated with elo1_pre and 2 other fieldsHigh correlation
qbelo2_pre is highly correlated with elo2_pre and 2 other fieldsHigh correlation
qb1_value_pre is highly correlated with qb1_value_postHigh correlation
qb2_value_pre is highly correlated with qb2_value_postHigh correlation
qbelo_prob1 is highly correlated with elo_prob1 and 2 other fieldsHigh correlation
qbelo_prob2 is highly correlated with elo_prob1 and 2 other fieldsHigh correlation
qb1_value_post is highly correlated with qb1_value_preHigh correlation
qb2_value_post is highly correlated with qb2_value_preHigh correlation
qbelo1_post is highly correlated with elo1_pre and 2 other fieldsHigh correlation
qbelo2_post is highly correlated with elo2_pre and 2 other fieldsHigh correlation
neutral is highly correlated with playoffHigh correlation
playoff is highly correlated with neutralHigh correlation
playoff has 16220 (96.5%) missing values Missing
qbelo1_pre has 2162 (12.9%) missing values Missing
qbelo2_pre has 2162 (12.9%) missing values Missing
qb1 has 2162 (12.9%) missing values Missing
qb2 has 2162 (12.9%) missing values Missing
qb1_value_pre has 2162 (12.9%) missing values Missing
qb2_value_pre has 2162 (12.9%) missing values Missing
qb1_adj has 2162 (12.9%) missing values Missing
qb2_adj has 2162 (12.9%) missing values Missing
qbelo_prob1 has 2162 (12.9%) missing values Missing
qbelo_prob2 has 2162 (12.9%) missing values Missing
qb1_game_value has 2162 (12.9%) missing values Missing
qb2_game_value has 2162 (12.9%) missing values Missing
qb1_value_post has 2162 (12.9%) missing values Missing
qb2_value_post has 2162 (12.9%) missing values Missing
qbelo1_post has 2162 (12.9%) missing values Missing
qbelo2_post has 2162 (12.9%) missing values Missing
score1 has 552 (3.3%) zeros Zeros
score2 has 967 (5.8%) zeros Zeros

Reproduction

Analysis started2021-04-16 01:44:39.340967
Analysis finished2021-04-16 01:45:36.821951
Duration57.48 seconds
Software versionpandas-profiling v2.11.0
Download configurationconfig.yaml

Variables

date
Categorical

HIGH CARDINALITY

Distinct3499
Distinct (%)20.8%
Missing0
Missing (%)0.0%
Memory size131.5 KiB
2012-01-01
 
16
2008-12-28
 
16
2016-01-03
 
16
2018-12-30
 
16
2017-12-31
 
16
Other values (3494)
16730 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters168100
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1522 ?
Unique (%)9.1%

Sample

1st row1920-09-26
2nd row1920-10-03
3rd row1920-10-03
4th row1920-10-03
5th row1920-10-03
ValueCountFrequency (%)
2012-01-0116
 
0.1%
2008-12-2816
 
0.1%
2016-01-0316
 
0.1%
2018-12-3016
 
0.1%
2017-12-3116
 
0.1%
2013-12-2916
 
0.1%
2017-01-0116
 
0.1%
2010-01-0316
 
0.1%
2005-01-0216
 
0.1%
2012-12-3016
 
0.1%
Other values (3489)16650
99.0%
2021-04-15T21:45:37.026505image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2012-01-0116
 
0.1%
2008-12-2816
 
0.1%
2016-01-0316
 
0.1%
2018-12-3016
 
0.1%
2017-12-3116
 
0.1%
2013-12-2916
 
0.1%
2017-01-0116
 
0.1%
2010-01-0316
 
0.1%
2005-01-0216
 
0.1%
2012-12-3016
 
0.1%
Other values (3489)16650
99.0%

Most occurring characters

ValueCountFrequency (%)
141245
24.5%
-33620
20.0%
025162
15.0%
920187
12.0%
219127
11.4%
85666
 
3.4%
75395
 
3.2%
65058
 
3.0%
34644
 
2.8%
44061
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number134480
80.0%
Dash Punctuation33620
 
20.0%

Most frequent character per category

ValueCountFrequency (%)
141245
30.7%
025162
18.7%
920187
15.0%
219127
14.2%
85666
 
4.2%
75395
 
4.0%
65058
 
3.8%
34644
 
3.5%
44061
 
3.0%
53935
 
2.9%
ValueCountFrequency (%)
-33620
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common168100
100.0%

Most frequent character per script

ValueCountFrequency (%)
141245
24.5%
-33620
20.0%
025162
15.0%
920187
12.0%
219127
11.4%
85666
 
3.4%
75395
 
3.2%
65058
 
3.0%
34644
 
2.8%
44061
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII168100
100.0%

Most frequent character per block

ValueCountFrequency (%)
141245
24.5%
-33620
20.0%
025162
15.0%
920187
12.0%
219127
11.4%
85666
 
3.4%
75395
 
3.2%
65058
 
3.0%
34644
 
2.8%
44061
 
2.4%

season
Real number (ℝ≥0)

Distinct101
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1983.619393
Minimum1920
Maximum2020
Zeros0
Zeros (%)0.0%
Memory size131.5 KiB
2021-04-15T21:45:37.131804image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1920
5-th percentile1930
Q11968
median1988
Q32005
95-th percentile2017
Maximum2020
Range100
Interquartile range (IQR)37

Descriptive statistics

Standard deviation25.87218417
Coefficient of variation (CV)0.01304291754
Kurtosis-0.37265495
Mean1983.619393
Median Absolute Deviation (MAD)18
Skewness-0.6635571224
Sum33344642
Variance669.3699136
MonotocityIncreasing
2021-04-15T21:45:37.261804image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2020269
 
1.6%
2015267
 
1.6%
2012267
 
1.6%
2007267
 
1.6%
2005267
 
1.6%
2008267
 
1.6%
2004267
 
1.6%
2009267
 
1.6%
2019267
 
1.6%
2003267
 
1.6%
Other values (91)14138
84.1%
ValueCountFrequency (%)
192090
0.5%
192166
0.4%
192274
0.4%
192388
0.5%
192480
0.5%
ValueCountFrequency (%)
2020269
1.6%
2019267
1.6%
2018267
1.6%
2017267
1.6%
2016267
1.6%

neutral
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size131.5 KiB
0
16718 
1
 
92

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters16810
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
016718
99.5%
192
 
0.5%
2021-04-15T21:45:37.465339image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-04-15T21:45:37.526278image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
016718
99.5%
192
 
0.5%

Most occurring characters

ValueCountFrequency (%)
016718
99.5%
192
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number16810
100.0%

Most frequent character per category

ValueCountFrequency (%)
016718
99.5%
192
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Common16810
100.0%

Most frequent character per script

ValueCountFrequency (%)
016718
99.5%
192
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII16810
100.0%

Most frequent character per block

ValueCountFrequency (%)
016718
99.5%
192
 
0.5%

playoff
Categorical

HIGH CORRELATION
MISSING

Distinct4
Distinct (%)0.7%
Missing16220
Missing (%)96.5%
Memory size131.5 KiB
d
226 
w
156 
c
153 
s
55 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters590
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowc
2nd rowc
3rd rowc
4th rowc
5th rowc
ValueCountFrequency (%)
d226
 
1.3%
w156
 
0.9%
c153
 
0.9%
s55
 
0.3%
(Missing)16220
96.5%
2021-04-15T21:45:37.691711image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-04-15T21:45:37.750730image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
d226
38.3%
w156
26.4%
c153
25.9%
s55
 
9.3%

Most occurring characters

ValueCountFrequency (%)
d226
38.3%
w156
26.4%
c153
25.9%
s55
 
9.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter590
100.0%

Most frequent character per category

ValueCountFrequency (%)
d226
38.3%
w156
26.4%
c153
25.9%
s55
 
9.3%

Most occurring scripts

ValueCountFrequency (%)
Latin590
100.0%

Most frequent character per script

ValueCountFrequency (%)
d226
38.3%
w156
26.4%
c153
25.9%
s55
 
9.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII590
100.0%

Most frequent character per block

ValueCountFrequency (%)
d226
38.3%
w156
26.4%
c153
25.9%
s55
 
9.3%

team1
Categorical

HIGH CARDINALITY

Distinct101
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size131.5 KiB
CHI
 
752
NYG
 
725
GB
 
724
ARI
 
680
DET
 
659
Other values (96)
13270 

Length

Max length3
Median length3
Mean length2.816537775
Min length2

Characters and Unicode

Total characters47346
Distinct characters26
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17 ?
Unique (%)0.1%

Sample

1st rowRII
2nd rowCBD
3rd rowCHI
4th rowRII
5th rowDAY
ValueCountFrequency (%)
CHI752
 
4.5%
NYG725
 
4.3%
GB724
 
4.3%
ARI680
 
4.0%
DET659
 
3.9%
WSH658
 
3.9%
PIT648
 
3.9%
PHI642
 
3.8%
LAR609
 
3.6%
SF590
 
3.5%
Other values (91)10123
60.2%
2021-04-15T21:45:37.968722image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
chi752
 
4.5%
nyg725
 
4.3%
gb724
 
4.3%
ari680
 
4.0%
det659
 
3.9%
wsh658
 
3.9%
pit648
 
3.9%
phi642
 
3.8%
lar609
 
3.6%
sf590
 
3.5%
Other values (91)10123
60.2%

Most occurring characters

ValueCountFrequency (%)
A4898
 
10.3%
I4749
 
10.0%
N4731
 
10.0%
C3092
 
6.5%
E3067
 
6.5%
L2910
 
6.1%
T2679
 
5.7%
D2334
 
4.9%
H2249
 
4.8%
B2159
 
4.6%
Other values (16)14478
30.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter47345
> 99.9%
Decimal Number1
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
A4898
 
10.3%
I4749
 
10.0%
N4731
 
10.0%
C3092
 
6.5%
E3067
 
6.5%
L2910
 
6.1%
T2679
 
5.7%
D2334
 
4.9%
H2249
 
4.8%
B2159
 
4.6%
Other values (15)14477
30.6%
ValueCountFrequency (%)
11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin47345
> 99.9%
Common1
 
< 0.1%

Most frequent character per script

ValueCountFrequency (%)
A4898
 
10.3%
I4749
 
10.0%
N4731
 
10.0%
C3092
 
6.5%
E3067
 
6.5%
L2910
 
6.1%
T2679
 
5.7%
D2334
 
4.9%
H2249
 
4.8%
B2159
 
4.6%
Other values (15)14477
30.6%
ValueCountFrequency (%)
11
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII47346
100.0%

Most frequent character per block

ValueCountFrequency (%)
A4898
 
10.3%
I4749
 
10.0%
N4731
 
10.0%
C3092
 
6.5%
E3067
 
6.5%
L2910
 
6.1%
T2679
 
5.7%
D2334
 
4.9%
H2249
 
4.8%
B2159
 
4.6%
Other values (16)14478
30.6%

team2
Categorical

HIGH CARDINALITY

Distinct108
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size131.5 KiB
GB
 
720
ARI
 
704
CHI
 
703
NYG
 
677
DET
 
642
Other values (103)
13364 

Length

Max length3
Median length3
Mean length2.817965497
Min length2

Characters and Unicode

Total characters47370
Distinct characters26
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17 ?
Unique (%)0.1%

Sample

1st rowSTP
2nd rowPTQ
3rd rowMUT
4th rowMUN
5th rowCOL
ValueCountFrequency (%)
GB720
 
4.3%
ARI704
 
4.2%
CHI703
 
4.2%
NYG677
 
4.0%
DET642
 
3.8%
WSH635
 
3.8%
PIT634
 
3.8%
PHI630
 
3.7%
LAR623
 
3.7%
SF569
 
3.4%
Other values (98)10273
61.1%
2021-04-15T21:45:38.198930image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
gb720
 
4.3%
ari704
 
4.2%
chi703
 
4.2%
nyg677
 
4.0%
det642
 
3.8%
wsh635
 
3.8%
pit634
 
3.8%
phi630
 
3.7%
lar623
 
3.7%
sf569
 
3.4%
Other values (98)10273
61.1%

Most occurring characters

ValueCountFrequency (%)
A5052
 
10.7%
N4717
 
10.0%
I4680
 
9.9%
C3115
 
6.6%
E3043
 
6.4%
L3018
 
6.4%
T2654
 
5.6%
D2356
 
5.0%
H2194
 
4.6%
B2148
 
4.5%
Other values (16)14393
30.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter47369
> 99.9%
Decimal Number1
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
A5052
 
10.7%
N4717
 
10.0%
I4680
 
9.9%
C3115
 
6.6%
E3043
 
6.4%
L3018
 
6.4%
T2654
 
5.6%
D2356
 
5.0%
H2194
 
4.6%
B2148
 
4.5%
Other values (15)14392
30.4%
ValueCountFrequency (%)
11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin47369
> 99.9%
Common1
 
< 0.1%

Most frequent character per script

ValueCountFrequency (%)
A5052
 
10.7%
N4717
 
10.0%
I4680
 
9.9%
C3115
 
6.6%
E3043
 
6.4%
L3018
 
6.4%
T2654
 
5.6%
D2356
 
5.0%
H2194
 
4.6%
B2148
 
4.5%
Other values (15)14392
30.4%
ValueCountFrequency (%)
11
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII47370
100.0%

Most frequent character per block

ValueCountFrequency (%)
A5052
 
10.7%
N4717
 
10.0%
I4680
 
9.9%
C3115
 
6.6%
E3043
 
6.4%
L3018
 
6.4%
T2654
 
5.6%
D2356
 
5.0%
H2194
 
4.6%
B2148
 
4.5%
Other values (16)14393
30.4%

elo1_pre
Real number (ℝ≥0)

HIGH CORRELATION

Distinct16462
Distinct (%)97.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1502.818325
Minimum1119.595
Maximum1839.663
Zeros0
Zeros (%)0.0%
Memory size131.5 KiB
2021-04-15T21:45:38.307610image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1119.595
5-th percentile1327.8175
Q11429.490394
median1504.603
Q31578.372582
95-th percentile1672.105309
Maximum1839.663
Range720.068
Interquartile range (IQR)148.882188

Descriptive statistics

Standard deviation105.0422755
Coefficient of variation (CV)0.06989685561
Kurtosis-0.3502584782
Mean1502.818325
Median Absolute Deviation (MAD)74.3525
Skewness-0.09101007731
Sum25262376.05
Variance11033.87964
MonotocityNot monotonic
2021-04-15T21:45:38.520756image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
130046
 
0.3%
1368.3333
 
< 0.1%
1473.983
 
< 0.1%
1413.8893
 
< 0.1%
1569.5452
 
< 0.1%
1383.7172
 
< 0.1%
1577.932
 
< 0.1%
1393.4592
 
< 0.1%
1522.7432
 
< 0.1%
1512.2012
 
< 0.1%
Other values (16452)16743
99.6%
ValueCountFrequency (%)
1119.5951
< 0.1%
1136.9951
< 0.1%
1147.3171
< 0.1%
1150.0871
< 0.1%
1153.9021
< 0.1%
ValueCountFrequency (%)
1839.6631
< 0.1%
1831.4621
< 0.1%
1824.2241
< 0.1%
1821.8151
< 0.1%
1810.5021
< 0.1%

elo2_pre
Real number (ℝ≥0)

HIGH CORRELATION

Distinct16449
Distinct (%)97.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1499.215536
Minimum1156.551
Maximum1849.484
Zeros0
Zeros (%)0.0%
Memory size131.5 KiB
2021-04-15T21:45:38.655607image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1156.551
5-th percentile1323.443493
Q11426.062
median1500.7065
Q31576.15875
95-th percentile1667.133823
Maximum1849.484
Range692.933
Interquartile range (IQR)150.09675

Descriptive statistics

Standard deviation104.455655
Coefficient of variation (CV)0.06967354087
Kurtosis-0.3978377632
Mean1499.215536
Median Absolute Deviation (MAD)74.985
Skewness-0.09526302666
Sum25201813.17
Variance10910.98385
MonotocityNot monotonic
2021-04-15T21:45:38.776141image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
130046
 
0.3%
1509.2133
 
< 0.1%
1552.3693
 
< 0.1%
1529.7413
 
< 0.1%
1488.0233
 
< 0.1%
1455.0323
 
< 0.1%
1466.5543
 
< 0.1%
1523.1253
 
< 0.1%
1596.4192
 
< 0.1%
1553.7752
 
< 0.1%
Other values (16439)16739
99.6%
ValueCountFrequency (%)
1156.5511
< 0.1%
1165.7161
< 0.1%
1166.9331
< 0.1%
1169.3441
< 0.1%
1172.9161
< 0.1%
ValueCountFrequency (%)
1849.4841
< 0.1%
1825.9611
< 0.1%
1809.4391
< 0.1%
1806.3461
< 0.1%
1804.3241
< 0.1%

elo_prob1
Real number (ℝ≥0)

HIGH CORRELATION

Distinct16661
Distinct (%)99.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5848954648
Minimum0.0709532918
Maximum0.9705164087
Zeros0
Zeros (%)0.0%
Memory size131.5 KiB
2021-04-15T21:45:38.908046image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0.0709532918
5-th percentile0.2753582581
Q10.4609668533
median0.5966977469
Q30.7196470275
95-th percentile0.8502683046
Maximum0.9705164087
Range0.8995631169
Interquartile range (IQR)0.2586801742

Descriptive statistics

Standard deviation0.175217634
Coefficient of variation (CV)0.2995708542
Kurtosis-0.6328963156
Mean0.5848954648
Median Absolute Deviation (MAD)0.1291537543
Skewness-0.2763349206
Sum9832.092763
Variance0.03070121927
MonotocityNot monotonic
2021-04-15T21:45:39.027740image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.59246623067
 
< 0.1%
0.83806750682
 
< 0.1%
0.65857245142
 
< 0.1%
0.58865800272
 
< 0.1%
0.40607658462
 
< 0.1%
0.6708987752
 
< 0.1%
0.75559542992
 
< 0.1%
0.68634239312
 
< 0.1%
0.83498571382
 
< 0.1%
0.76172302272
 
< 0.1%
Other values (16651)16785
99.9%
ValueCountFrequency (%)
0.07095329181
< 0.1%
0.074040128841
< 0.1%
0.087648076941
< 0.1%
0.10374969241
< 0.1%
0.10591910771
< 0.1%
ValueCountFrequency (%)
0.97051640871
< 0.1%
0.96457805711
< 0.1%
0.96214250271
< 0.1%
0.95675549171
< 0.1%
0.95656120561
< 0.1%

elo_prob2
Real number (ℝ≥0)

HIGH CORRELATION

Distinct16661
Distinct (%)99.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4151045352
Minimum0.02948359131
Maximum0.9290467082
Zeros0
Zeros (%)0.0%
Memory size131.5 KiB
2021-04-15T21:45:39.158807image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0.02948359131
5-th percentile0.1497316954
Q10.2803529725
median0.4033022531
Q30.5390331467
95-th percentile0.7246417419
Maximum0.9290467082
Range0.8995631169
Interquartile range (IQR)0.2586801742

Descriptive statistics

Standard deviation0.175217634
Coefficient of variation (CV)0.4221048414
Kurtosis-0.6328963156
Mean0.4151045352
Median Absolute Deviation (MAD)0.1291537543
Skewness0.2763349206
Sum6977.907237
Variance0.03070121927
MonotocityNot monotonic
2021-04-15T21:45:39.276573image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.40753376947
 
< 0.1%
0.21680230712
 
< 0.1%
0.41359074732
 
< 0.1%
0.17578790272
 
< 0.1%
0.33969264042
 
< 0.1%
0.22204340032
 
< 0.1%
0.16501428622
 
< 0.1%
0.71221832142
 
< 0.1%
0.47304274612
 
< 0.1%
0.542615072
 
< 0.1%
Other values (16651)16785
99.9%
ValueCountFrequency (%)
0.029483591311
< 0.1%
0.035421942851
< 0.1%
0.03785749731
< 0.1%
0.043244508331
< 0.1%
0.043438794421
< 0.1%
ValueCountFrequency (%)
0.92904670821
< 0.1%
0.92595987121
< 0.1%
0.91235192311
< 0.1%
0.89625030761
< 0.1%
0.89408089231
< 0.1%

elo1_post
Real number (ℝ≥0)

HIGH CORRELATION

Distinct16495
Distinct (%)98.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1502.581784
Minimum1119.595
Maximum1849.484
Zeros0
Zeros (%)0.0%
Memory size131.5 KiB
2021-04-15T21:45:39.401483image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1119.595
5-th percentile1323.4869
Q11427.0375
median1504.4145
Q31580.8905
95-th percentile1675.19575
Maximum1849.484
Range729.889
Interquartile range (IQR)153.853

Descriptive statistics

Standard deviation107.6117241
Coefficient of variation (CV)0.07161788146
Kurtosis-0.3754598783
Mean1502.581784
Median Absolute Deviation (MAD)76.905
Skewness-0.09235527703
Sum25258399.79
Variance11580.28316
MonotocityNot monotonic
2021-04-15T21:45:39.519383image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1422.7213
 
< 0.1%
1480.4123
 
< 0.1%
1488.0233
 
< 0.1%
1466.5543
 
< 0.1%
1670.5672
 
< 0.1%
1534.1852
 
< 0.1%
1691.4892
 
< 0.1%
1464.1312
 
< 0.1%
1596.1812
 
< 0.1%
1373.8032
 
< 0.1%
Other values (16485)16786
99.9%
ValueCountFrequency (%)
1119.5951
< 0.1%
1136.9951
< 0.1%
1139.9721
< 0.1%
1145.0671
< 0.1%
1147.3171
< 0.1%
ValueCountFrequency (%)
1849.4841
< 0.1%
1839.6631
< 0.1%
1825.9611
< 0.1%
1824.2241
< 0.1%
1821.8151
< 0.1%

elo2_post
Real number (ℝ≥0)

HIGH CORRELATION

Distinct16552
Distinct (%)98.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1499.452079
Minimum1153.902
Maximum1831.462
Zeros0
Zeros (%)0.0%
Memory size131.5 KiB
2021-04-15T21:45:39.646821image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1153.902
5-th percentile1320.376758
Q11424.364
median1500.787
Q31577.14975
95-th percentile1670.0828
Maximum1831.462
Range677.56
Interquartile range (IQR)152.78575

Descriptive statistics

Standard deviation106.6445704
Coefficient of variation (CV)0.07112235986
Kurtosis-0.399280129
Mean1499.452079
Median Absolute Deviation (MAD)76.392
Skewness-0.09220824329
Sum25205789.45
Variance11373.06439
MonotocityNot monotonic
2021-04-15T21:45:39.768548image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1427.4623
 
< 0.1%
1500.7873
 
< 0.1%
1479.3623
 
< 0.1%
1510.413
 
< 0.1%
1442.0822
 
< 0.1%
1496.6422
 
< 0.1%
1670.2852
 
< 0.1%
1624.8992
 
< 0.1%
1728.2162
 
< 0.1%
1530.0622
 
< 0.1%
Other values (16542)16786
99.9%
ValueCountFrequency (%)
1153.9021
< 0.1%
1156.5511
< 0.1%
1163.6131
< 0.1%
1165.7161
< 0.1%
1166.6151
< 0.1%
ValueCountFrequency (%)
1831.4621
< 0.1%
1824.2951
< 0.1%
1816.5061
< 0.1%
1810.5021
< 0.1%
1809.4391
< 0.1%

qbelo1_pre
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct14636
Distinct (%)99.9%
Missing2162
Missing (%)12.9%
Infinite0
Infinite (%)0.0%
Mean1504.129467
Minimum1149.699743
Maximum1806.39016
Zeros0
Zeros (%)0.0%
Memory size131.5 KiB
2021-04-15T21:45:39.898810image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1149.699743
5-th percentile1336.72413
Q11433.940308
median1505.720834
Q31574.571897
95-th percentile1667.632741
Maximum1806.39016
Range656.6904168
Interquartile range (IQR)140.6315887

Descriptive statistics

Standard deviation100.1581641
Coefficient of variation (CV)0.06658879191
Kurtosis-0.3570398292
Mean1504.129467
Median Absolute Deviation (MAD)70.377696
Skewness-0.06508133542
Sum22032488.43
Variance10031.65783
MonotocityNot monotonic
2021-04-15T21:45:40.020983image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
130013
 
0.1%
1568.7104281
 
< 0.1%
1476.2661391
 
< 0.1%
1331.6859931
 
< 0.1%
1346.9387791
 
< 0.1%
1425.0396241
 
< 0.1%
1560.6743181
 
< 0.1%
1455.4076531
 
< 0.1%
1327.1495291
 
< 0.1%
1598.0014291
 
< 0.1%
Other values (14626)14626
87.0%
(Missing)2162
 
12.9%
ValueCountFrequency (%)
1149.6997431
< 0.1%
1171.9711311
< 0.1%
1172.7613851
< 0.1%
1178.6190451
< 0.1%
1181.149771
< 0.1%
ValueCountFrequency (%)
1806.390161
< 0.1%
1800.2565921
< 0.1%
1793.9137211
< 0.1%
1792.0622231
< 0.1%
1789.8741631
< 0.1%

qbelo2_pre
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct14642
Distinct (%)> 99.9%
Missing2162
Missing (%)12.9%
Infinite0
Infinite (%)0.0%
Mean1502.829102
Minimum1152.474651
Maximum1814.366226
Zeros0
Zeros (%)0.0%
Memory size131.5 KiB
2021-04-15T21:45:40.237738image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1152.474651
5-th percentile1336.829078
Q11434.31213
median1504.449776
Q31574.782379
95-th percentile1660.70084
Maximum1814.366226
Range661.8915753
Interquartile range (IQR)140.4702492

Descriptive statistics

Standard deviation98.90252947
Coefficient of variation (CV)0.06581089583
Kurtosis-0.3430604991
Mean1502.829102
Median Absolute Deviation (MAD)70.23489652
Skewness-0.1137080777
Sum22013440.68
Variance9781.710336
MonotocityNot monotonic
2021-04-15T21:45:40.358418image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13007
 
< 0.1%
1399.0813091
 
< 0.1%
1609.8875391
 
< 0.1%
1360.0545591
 
< 0.1%
1582.9384791
 
< 0.1%
1563.5101651
 
< 0.1%
1516.9311111
 
< 0.1%
1595.2039861
 
< 0.1%
1427.5697071
 
< 0.1%
1499.1793361
 
< 0.1%
Other values (14632)14632
87.0%
(Missing)2162
 
12.9%
ValueCountFrequency (%)
1152.4746511
< 0.1%
1162.5650681
< 0.1%
1164.3276761
< 0.1%
1165.2812441
< 0.1%
1183.6061811
< 0.1%
ValueCountFrequency (%)
1814.3662261
< 0.1%
1798.8358061
< 0.1%
1795.5140491
< 0.1%
1789.5599061
< 0.1%
1783.2193491
< 0.1%

qb1
Categorical

HIGH CARDINALITY
MISSING

Distinct625
Distinct (%)4.3%
Missing2162
Missing (%)12.9%
Memory size131.5 KiB
Tom Brady
 
179
Brett Favre
 
162
Drew Brees
 
154
Peyton Manning
 
151
John Elway
 
130
Other values (620)
13872 

Length

Max length18
Median length12
Mean length11.93323321
Min length7

Characters and Unicode

Total characters174798
Distinct characters53
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique85 ?
Unique (%)0.6%

Sample

1st rowTommy Thompson
2nd rowNorm Van Brocklin
3rd rowJoe Gasparella
4th rowAdrian Burk
5th rowTobin Rote
ValueCountFrequency (%)
Tom Brady179
 
1.1%
Brett Favre162
 
1.0%
Drew Brees154
 
0.9%
Peyton Manning151
 
0.9%
John Elway130
 
0.8%
Dan Marino130
 
0.8%
Ben Roethlisberger129
 
0.8%
Eli Manning126
 
0.7%
Fran Tarkenton126
 
0.7%
Philip Rivers125
 
0.7%
Other values (615)13236
78.7%
(Missing)2162
 
12.9%
2021-04-15T21:45:40.632380image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
jim578
 
2.0%
steve570
 
1.9%
joe517
 
1.8%
john366
 
1.2%
manning347
 
1.2%
matt332
 
1.1%
ryan317
 
1.1%
dan298
 
1.0%
drew272
 
0.9%
tom255
 
0.9%
Other values (804)25545
86.9%

Most occurring characters

ValueCountFrequency (%)
e16296
 
9.3%
14749
 
8.4%
n13427
 
7.7%
a13244
 
7.6%
r12920
 
7.4%
o11008
 
6.3%
i9264
 
5.3%
l7430
 
4.3%
t7120
 
4.1%
s5561
 
3.2%
Other values (43)63779
36.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter129521
74.1%
Uppercase Letter30159
 
17.3%
Space Separator14749
 
8.4%
Other Punctuation369
 
0.2%

Most frequent character per category

ValueCountFrequency (%)
B3467
11.5%
J3197
 
10.6%
M2850
 
9.4%
D2281
 
7.6%
S2014
 
6.7%
T1925
 
6.4%
C1805
 
6.0%
R1796
 
6.0%
K1429
 
4.7%
G1219
 
4.0%
Other values (15)8176
27.1%
ValueCountFrequency (%)
e16296
12.6%
n13427
10.4%
a13244
10.2%
r12920
10.0%
o11008
 
8.5%
i9264
 
7.2%
l7430
 
5.7%
t7120
 
5.5%
s5561
 
4.3%
y3884
 
3.0%
Other values (15)29367
22.7%
ValueCountFrequency (%)
.241
65.3%
'128
34.7%
ValueCountFrequency (%)
14749
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin159680
91.4%
Common15118
 
8.6%

Most frequent character per script

ValueCountFrequency (%)
e16296
 
10.2%
n13427
 
8.4%
a13244
 
8.3%
r12920
 
8.1%
o11008
 
6.9%
i9264
 
5.8%
l7430
 
4.7%
t7120
 
4.5%
s5561
 
3.5%
y3884
 
2.4%
Other values (40)59526
37.3%
ValueCountFrequency (%)
14749
97.6%
.241
 
1.6%
'128
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII174798
100.0%

Most frequent character per block

ValueCountFrequency (%)
e16296
 
9.3%
14749
 
8.4%
n13427
 
7.7%
a13244
 
7.6%
r12920
 
7.4%
o11008
 
6.3%
i9264
 
5.3%
l7430
 
4.3%
t7120
 
4.1%
s5561
 
3.2%
Other values (43)63779
36.5%

qb2
Categorical

HIGH CARDINALITY
MISSING

Distinct642
Distinct (%)4.4%
Missing2162
Missing (%)12.9%
Memory size131.5 KiB
Tom Brady
 
165
Brett Favre
 
160
Drew Brees
 
150
Peyton Manning
 
141
Dan Marino
 
128
Other values (637)
13904 

Length

Max length18
Median length12
Mean length11.94074276
Min length7

Characters and Unicode

Total characters174908
Distinct characters54
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique85 ?
Unique (%)0.6%

Sample

1st rowOtto Graham
2nd rowJohnny Lujack
3rd rowCharlie Conerly
4th rowSammy Baugh
5th rowBobby Layne
ValueCountFrequency (%)
Tom Brady165
 
1.0%
Brett Favre160
 
1.0%
Drew Brees150
 
0.9%
Peyton Manning141
 
0.8%
Dan Marino128
 
0.8%
Philip Rivers127
 
0.8%
Ben Roethlisberger124
 
0.7%
Fran Tarkenton124
 
0.7%
John Elway122
 
0.7%
Eli Manning120
 
0.7%
Other values (632)13287
79.0%
(Missing)2162
 
12.9%
2021-04-15T21:45:40.866066image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
steve590
 
2.0%
jim586
 
2.0%
joe504
 
1.7%
john360
 
1.2%
matt347
 
1.2%
manning330
 
1.1%
ryan313
 
1.1%
dan289
 
1.0%
drew267
 
0.9%
jeff265
 
0.9%
Other values (821)25545
86.9%

Most occurring characters

ValueCountFrequency (%)
e16443
 
9.4%
14748
 
8.4%
n13261
 
7.6%
a13195
 
7.5%
r12932
 
7.4%
o11024
 
6.3%
i9225
 
5.3%
l7557
 
4.3%
t7140
 
4.1%
s5622
 
3.2%
Other values (44)63761
36.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter129652
74.1%
Uppercase Letter30143
 
17.2%
Space Separator14748
 
8.4%
Other Punctuation365
 
0.2%

Most frequent character per category

ValueCountFrequency (%)
e16443
12.7%
n13261
10.2%
a13195
10.2%
r12932
10.0%
o11024
 
8.5%
i9225
 
7.1%
l7557
 
5.8%
t7140
 
5.5%
s5622
 
4.3%
y3848
 
3.0%
Other values (16)29405
22.7%
ValueCountFrequency (%)
B3476
11.5%
J3181
 
10.6%
M2860
 
9.5%
D2252
 
7.5%
S2047
 
6.8%
T1860
 
6.2%
C1838
 
6.1%
R1776
 
5.9%
K1442
 
4.8%
G1242
 
4.1%
Other values (15)8169
27.1%
ValueCountFrequency (%)
.244
66.8%
'121
33.2%
ValueCountFrequency (%)
14748
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin159795
91.4%
Common15113
 
8.6%

Most frequent character per script

ValueCountFrequency (%)
e16443
 
10.3%
n13261
 
8.3%
a13195
 
8.3%
r12932
 
8.1%
o11024
 
6.9%
i9225
 
5.8%
l7557
 
4.7%
t7140
 
4.5%
s5622
 
3.5%
y3848
 
2.4%
Other values (41)59548
37.3%
ValueCountFrequency (%)
14748
97.6%
.244
 
1.6%
'121
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII174908
100.0%

Most frequent character per block

ValueCountFrequency (%)
e16443
 
9.4%
14748
 
8.4%
n13261
 
7.6%
a13195
 
7.5%
r12932
 
7.4%
o11024
 
6.3%
i9225
 
5.3%
l7557
 
4.3%
t7140
 
4.1%
s5622
 
3.2%
Other values (44)63761
36.5%

qb1_value_pre
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct14484
Distinct (%)98.9%
Missing2162
Missing (%)12.9%
Infinite0
Infinite (%)0.0%
Mean94.77925616
Minimum-53.77891723
Maximum329.5627157
Zeros143
Zeros (%)0.9%
Memory size131.5 KiB
2021-04-15T21:45:40.976921image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum-53.77891723
5-th percentile8.521930517
Q153.15128642
median89.3158015
Q3130.3377177
95-th percentile199.1002613
Maximum329.5627157
Range383.341633
Interquartile range (IQR)77.18643125

Descriptive statistics

Standard deviation57.44808111
Coefficient of variation (CV)0.6061250472
Kurtosis0.1750762959
Mean94.77925616
Median Absolute Deviation (MAD)38.12342702
Skewness0.5372502959
Sum1388326.544
Variance3300.282023
MonotocityNot monotonic
2021-04-15T21:45:41.099916image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0143
 
0.9%
112.901587
 
< 0.1%
103.379764
 
< 0.1%
66.199322
 
< 0.1%
18.538400572
 
< 0.1%
111.994742
 
< 0.1%
16.323122
 
< 0.1%
32.646242
 
< 0.1%
98.845562
 
< 0.1%
39.382765712
 
< 0.1%
Other values (14474)14480
86.1%
(Missing)2162
 
12.9%
ValueCountFrequency (%)
-53.778917231
< 0.1%
-46.720907761
< 0.1%
-46.329533031
< 0.1%
-44.98544371
< 0.1%
-44.032285791
< 0.1%
ValueCountFrequency (%)
329.56271571
< 0.1%
317.4727581
< 0.1%
313.82838351
< 0.1%
308.7623811
< 0.1%
306.84413241
< 0.1%

qb2_value_pre
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct14508
Distinct (%)99.0%
Missing2162
Missing (%)12.9%
Infinite0
Infinite (%)0.0%
Mean94.76545474
Minimum-47.28686682
Maximum327.7165449
Zeros127
Zeros (%)0.8%
Memory size131.5 KiB
2021-04-15T21:45:41.230320image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum-47.28686682
5-th percentile8.304524481
Q153.49344329
median89.688689
Q3130.0991619
95-th percentile198.2504889
Maximum327.7165449
Range375.0034117
Interquartile range (IQR)76.60571864

Descriptive statistics

Standard deviation56.96941638
Coefficient of variation (CV)0.6011622752
Kurtosis0.1493478724
Mean94.76545474
Median Absolute Deviation (MAD)38.01095883
Skewness0.5261894035
Sum1388124.381
Variance3245.514403
MonotocityNot monotonic
2021-04-15T21:45:41.351754image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0127
 
0.8%
112.901585
 
< 0.1%
111.994742
 
< 0.1%
16.128797142
 
< 0.1%
112.448162
 
< 0.1%
61.289429142
 
< 0.1%
74.81432
 
< 0.1%
23.124422
 
< 0.1%
108.82082
 
< 0.1%
102.01952
 
< 0.1%
Other values (14498)14500
86.3%
(Missing)2162
 
12.9%
ValueCountFrequency (%)
-47.286866821
< 0.1%
-45.344434191
< 0.1%
-45.310723341
< 0.1%
-42.395557111
< 0.1%
-36.567947131
< 0.1%
ValueCountFrequency (%)
327.71654491
< 0.1%
310.1306781
< 0.1%
307.03426451
< 0.1%
300.76185811
< 0.1%
300.23408331
< 0.1%

qb1_adj
Real number (ℝ)

MISSING

Distinct14639
Distinct (%)99.9%
Missing2162
Missing (%)12.9%
Infinite0
Infinite (%)0.0%
Mean-1.255064318
Minimum-242.4876783
Maximum107.6857927
Zeros7
Zeros (%)< 0.1%
Memory size131.5 KiB
2021-04-15T21:45:41.483325image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum-242.4876783
5-th percentile-47.97217805
Q1-8.232100357
median2.000310928
Q312.05119042
95-th percentile28.7490017
Maximum107.6857927
Range350.173471
Interquartile range (IQR)20.28329078

Descriptive statistics

Standard deviation25.01341343
Coefficient of variation (CV)-19.92998532
Kurtosis8.607723311
Mean-1.255064318
Median Absolute Deviation (MAD)10.12860308
Skewness-2.031500984
Sum-18384.18212
Variance625.6708515
MonotocityNot monotonic
2021-04-15T21:45:41.604190image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
07
 
< 0.1%
-53.729907414
 
< 0.1%
10.024010861
 
< 0.1%
2.580330051
 
< 0.1%
-13.688156051
 
< 0.1%
-14.734155851
 
< 0.1%
-20.131769731
 
< 0.1%
15.627999191
 
< 0.1%
-162.77020351
 
< 0.1%
-16.430267551
 
< 0.1%
Other values (14629)14629
87.0%
(Missing)2162
 
12.9%
ValueCountFrequency (%)
-242.48767831
< 0.1%
-219.85682121
< 0.1%
-218.56667271
< 0.1%
-184.75720131
< 0.1%
-181.86745181
< 0.1%
ValueCountFrequency (%)
107.68579271
< 0.1%
96.527366681
< 0.1%
91.302402851
< 0.1%
87.753408741
< 0.1%
87.622454631
< 0.1%

qb2_adj
Real number (ℝ)

MISSING

Distinct14643
Distinct (%)> 99.9%
Missing2162
Missing (%)12.9%
Infinite0
Infinite (%)0.0%
Mean-1.277486581
Minimum-218.5685995
Maximum107.0874614
Zeros6
Zeros (%)< 0.1%
Memory size131.5 KiB
2021-04-15T21:45:41.733117image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum-218.5685995
5-th percentile-50.02761506
Q1-8.074793465
median2.145893642
Q312.0203684
95-th percentile29.15086191
Maximum107.0874614
Range325.6560609
Interquartile range (IQR)20.09516186

Descriptive statistics

Standard deviation25.53262038
Coefficient of variation (CV)-19.98660554
Kurtosis8.188142431
Mean-1.277486581
Median Absolute Deviation (MAD)10.03345362
Skewness-2.019629517
Sum-18712.62344
Variance651.9147034
MonotocityNot monotonic
2021-04-15T21:45:41.857868image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
06
 
< 0.1%
20.286651041
 
< 0.1%
-6.1637069651
 
< 0.1%
12.777173741
 
< 0.1%
-13.364650591
 
< 0.1%
5.9312760791
 
< 0.1%
-17.339019871
 
< 0.1%
-0.56001197171
 
< 0.1%
26.429358071
 
< 0.1%
9.7247405561
 
< 0.1%
Other values (14633)14633
87.0%
(Missing)2162
 
12.9%
ValueCountFrequency (%)
-218.56859951
< 0.1%
-205.04990021
< 0.1%
-196.87796911
< 0.1%
-196.30514571
< 0.1%
-195.75112491
< 0.1%
ValueCountFrequency (%)
107.08746141
< 0.1%
93.618488381
< 0.1%
90.255637161
< 0.1%
86.923832061
< 0.1%
82.038374521
< 0.1%

qbelo_prob1
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct14648
Distinct (%)100.0%
Missing2162
Missing (%)12.9%
Infinite0
Infinite (%)0.0%
Mean0.5750242051
Minimum0.05981049406
Maximum0.9671966037
Zeros0
Zeros (%)0.0%
Memory size131.5 KiB
2021-04-15T21:45:42.080106image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0.05981049406
5-th percentile0.2628552775
Q10.4467219925
median0.5863831495
Q30.7134845932
95-th percentile0.8476628419
Maximum0.9671966037
Range0.9073861096
Interquartile range (IQR)0.2667626007

Descriptive statistics

Standard deviation0.1780393333
Coefficient of variation (CV)0.3096205894
Kurtosis-0.6408765304
Mean0.5750242051
Median Absolute Deviation (MAD)0.1324194327
Skewness-0.2460345363
Sum8422.954556
Variance0.0316980042
MonotocityNot monotonic
2021-04-15T21:45:42.197299image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.65161635441
 
< 0.1%
0.60734197441
 
< 0.1%
0.53329586671
 
< 0.1%
0.72866813281
 
< 0.1%
0.76346833481
 
< 0.1%
0.61878513161
 
< 0.1%
0.51474698371
 
< 0.1%
0.3760816391
 
< 0.1%
0.17296929711
 
< 0.1%
0.72701451081
 
< 0.1%
Other values (14638)14638
87.1%
(Missing)2162
 
12.9%
ValueCountFrequency (%)
0.059810494061
< 0.1%
0.070228847641
< 0.1%
0.086213926911
< 0.1%
0.094097732631
< 0.1%
0.096356313541
< 0.1%
ValueCountFrequency (%)
0.96719660371
< 0.1%
0.96590984211
< 0.1%
0.96452923611
< 0.1%
0.95932450341
< 0.1%
0.95920375171
< 0.1%

qbelo_prob2
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct14648
Distinct (%)100.0%
Missing2162
Missing (%)12.9%
Infinite0
Infinite (%)0.0%
Mean0.4249757949
Minimum0.03280339633
Maximum0.9401895059
Zeros0
Zeros (%)0.0%
Memory size131.5 KiB
2021-04-15T21:45:42.327327image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0.03280339633
5-th percentile0.1523371581
Q10.2865154068
median0.4136168505
Q30.5532780075
95-th percentile0.7371447225
Maximum0.9401895059
Range0.9073861096
Interquartile range (IQR)0.2667626007

Descriptive statistics

Standard deviation0.1780393333
Coefficient of variation (CV)0.4189399383
Kurtosis-0.6408765304
Mean0.4249757949
Median Absolute Deviation (MAD)0.1324194327
Skewness0.2460345363
Sum6225.045444
Variance0.0316980042
MonotocityNot monotonic
2021-04-15T21:45:42.443143image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.56594500131
 
< 0.1%
0.57210440541
 
< 0.1%
0.22247397551
 
< 0.1%
0.19411815881
 
< 0.1%
0.54189548991
 
< 0.1%
0.24597152051
 
< 0.1%
0.4054194781
 
< 0.1%
0.63305603271
 
< 0.1%
0.2999584151
 
< 0.1%
0.25539070511
 
< 0.1%
Other values (14638)14638
87.1%
(Missing)2162
 
12.9%
ValueCountFrequency (%)
0.032803396331
< 0.1%
0.034090157931
< 0.1%
0.035470763911
< 0.1%
0.040675496571
< 0.1%
0.040796248331
< 0.1%
ValueCountFrequency (%)
0.94018950591
< 0.1%
0.92977115241
< 0.1%
0.91378607311
< 0.1%
0.90590226741
< 0.1%
0.90364368651
< 0.1%

qb1_game_value
Real number (ℝ)

MISSING

Distinct14648
Distinct (%)100.0%
Missing2162
Missing (%)12.9%
Infinite0
Infinite (%)0.0%
Mean107.8549614
Minimum-385.7371097
Maximum713.6952217
Zeros0
Zeros (%)0.0%
Memory size131.5 KiB
2021-04-15T21:45:42.568211image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum-385.7371097
5-th percentile-101.7499371
Q114.71708028
median104.8991855
Q3198.56109
95-th percentile332.0016659
Maximum713.6952217
Range1099.432331
Interquartile range (IQR)183.8440097

Descriptive statistics

Standard deviation133.2980202
Coefficient of variation (CV)1.23590068
Kurtosis-0.01697591414
Mean107.8549614
Median Absolute Deviation (MAD)92.03637607
Skewness0.1244690453
Sum1579859.475
Variance17768.3622
MonotocityNot monotonic
2021-04-15T21:45:42.684209image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
141.78209781
 
< 0.1%
-10.337018661
 
< 0.1%
218.59620761
 
< 0.1%
-43.031608091
 
< 0.1%
128.6265111
 
< 0.1%
148.73995821
 
< 0.1%
45.187668041
 
< 0.1%
-4.9446829541
 
< 0.1%
251.3697661
 
< 0.1%
163.35456861
 
< 0.1%
Other values (14638)14638
87.1%
(Missing)2162
 
12.9%
ValueCountFrequency (%)
-385.73710971
< 0.1%
-381.03750051
< 0.1%
-358.0120521
< 0.1%
-357.9511
< 0.1%
-337.63189291
< 0.1%
ValueCountFrequency (%)
713.69522171
< 0.1%
634.03792121
< 0.1%
628.58048321
< 0.1%
609.98180661
< 0.1%
578.58563651
< 0.1%

qb2_game_value
Real number (ℝ)

MISSING

Distinct14648
Distinct (%)100.0%
Missing2162
Missing (%)12.9%
Infinite0
Infinite (%)0.0%
Mean87.25372126
Minimum-413.9716592
Maximum605.0981794
Zeros0
Zeros (%)0.0%
Memory size131.5 KiB
2021-04-15T21:45:42.808292image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum-413.9716592
5-th percentile-120.34873
Q1-6.171506728
median82.77768711
Q3175.6722255
95-th percentile310.6755068
Maximum605.0981794
Range1019.069839
Interquartile range (IQR)181.8437323

Descriptive statistics

Standard deviation131.595488
Coefficient of variation (CV)1.508193417
Kurtosis-0.08514296754
Mean87.25372126
Median Absolute Deviation (MAD)90.71727087
Skewness0.1677685271
Sum1278092.509
Variance17317.37246
MonotocityNot monotonic
2021-04-15T21:45:42.929229image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
352.75847921
 
< 0.1%
110.33368341
 
< 0.1%
259.84601211
 
< 0.1%
93.87072531
 
< 0.1%
9.8146609261
 
< 0.1%
-87.763673191
 
< 0.1%
49.4713471
 
< 0.1%
301.36735031
 
< 0.1%
-52.212713061
 
< 0.1%
166.97263781
 
< 0.1%
Other values (14638)14638
87.1%
(Missing)2162
 
12.9%
ValueCountFrequency (%)
-413.97165921
< 0.1%
-351.04933581
< 0.1%
-340.82789171
< 0.1%
-331.04108591
< 0.1%
-324.9743191
< 0.1%
ValueCountFrequency (%)
605.09817941
< 0.1%
600.4173971
< 0.1%
568.78440141
< 0.1%
563.90584081
< 0.1%
562.29727991
< 0.1%

qb1_value_post
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct14648
Distinct (%)100.0%
Missing2162
Missing (%)12.9%
Infinite0
Infinite (%)0.0%
Mean96.08682669
Minimum-46.32953303
Maximum327.7165449
Zeros0
Zeros (%)0.0%
Memory size131.5 KiB
2021-04-15T21:45:43.051081image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum-46.32953303
5-th percentile10.83280508
Q153.92967583
median90.5079474
Q3131.832023
95-th percentile200.7191423
Maximum327.7165449
Range374.0460779
Interquartile range (IQR)77.90234718

Descriptive statistics

Standard deviation57.77102922
Coefficient of variation (CV)0.6012377681
Kurtosis0.1610348444
Mean96.08682669
Median Absolute Deviation (MAD)38.76495716
Skewness0.5377336055
Sum1407479.837
Variance3337.491817
MonotocityNot monotonic
2021-04-15T21:45:43.174201image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.47228721
 
< 0.1%
93.701953561
 
< 0.1%
28.23803341
 
< 0.1%
33.63512991
 
< 0.1%
115.78679461
 
< 0.1%
53.799056481
 
< 0.1%
87.787459161
 
< 0.1%
35.102311061
 
< 0.1%
134.05500951
 
< 0.1%
263.86335881
 
< 0.1%
Other values (14638)14638
87.1%
(Missing)2162
 
12.9%
ValueCountFrequency (%)
-46.329533031
< 0.1%
-46.175670851
< 0.1%
-45.582580791
< 0.1%
-45.344434191
< 0.1%
-45.310723341
< 0.1%
ValueCountFrequency (%)
327.71654491
< 0.1%
317.4727581
< 0.1%
310.1306781
< 0.1%
307.03426451
< 0.1%
306.84413241
< 0.1%

qb2_value_post
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct14648
Distinct (%)100.0%
Missing2162
Missing (%)12.9%
Infinite0
Infinite (%)0.0%
Mean94.0142814
Minimum-53.77891723
Maximum329.5627157
Zeros0
Zeros (%)0.0%
Memory size131.5 KiB
2021-04-15T21:45:43.305073image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum-53.77891723
5-th percentile9.23289783
Q152.35154519
median88.75857522
Q3129.9368506
95-th percentile197.5245344
Maximum329.5627157
Range383.341633
Interquartile range (IQR)77.58530541

Descriptive statistics

Standard deviation57.17103537
Coefficient of variation (CV)0.6081101139
Kurtosis0.1215686197
Mean94.0142814
Median Absolute Deviation (MAD)38.34189743
Skewness0.5221166763
Sum1377121.194
Variance3268.527285
MonotocityNot monotonic
2021-04-15T21:45:43.429533image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
189.12378331
 
< 0.1%
76.115079851
 
< 0.1%
25.679273021
 
< 0.1%
61.386634081
 
< 0.1%
46.690215711
 
< 0.1%
88.100790531
 
< 0.1%
132.54026731
 
< 0.1%
151.86078711
 
< 0.1%
68.639734461
 
< 0.1%
62.963266551
 
< 0.1%
Other values (14638)14638
87.1%
(Missing)2162
 
12.9%
ValueCountFrequency (%)
-53.778917231
< 0.1%
-47.286866821
< 0.1%
-46.720907761
< 0.1%
-44.98544371
< 0.1%
-44.168265591
< 0.1%
ValueCountFrequency (%)
329.56271571
< 0.1%
313.82838351
< 0.1%
308.7623811
< 0.1%
305.25446191
< 0.1%
303.40665091
< 0.1%

qbelo1_post
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct14648
Distinct (%)100.0%
Missing2162
Missing (%)12.9%
Infinite0
Infinite (%)0.0%
Mean1504.174053
Minimum1164.327676
Maximum1814.366226
Zeros0
Zeros (%)0.0%
Memory size131.5 KiB
2021-04-15T21:45:43.561507image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1164.327676
5-th percentile1334.094904
Q11432.405966
median1505.163207
Q31578.430253
95-th percentile1670.188472
Maximum1814.366226
Range650.0385505
Interquartile range (IQR)146.0242875

Descriptive statistics

Standard deviation102.6279434
Coefficient of variation (CV)0.06822876858
Kurtosis-0.3895373628
Mean1504.174053
Median Absolute Deviation (MAD)73.06363961
Skewness-0.07067251212
Sum22033141.53
Variance10532.49476
MonotocityNot monotonic
2021-04-15T21:45:43.768607image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1436.3275141
 
< 0.1%
1488.2780091
 
< 0.1%
1439.3571141
 
< 0.1%
1557.7520151
 
< 0.1%
1565.7886881
 
< 0.1%
1433.674351
 
< 0.1%
1303.0647951
 
< 0.1%
1302.2721491
 
< 0.1%
1497.5492771
 
< 0.1%
1531.2597991
 
< 0.1%
Other values (14638)14638
87.1%
(Missing)2162
 
12.9%
ValueCountFrequency (%)
1164.3276761
< 0.1%
1165.2812441
< 0.1%
1171.9711311
< 0.1%
1172.7613851
< 0.1%
1176.4162941
< 0.1%
ValueCountFrequency (%)
1814.3662261
< 0.1%
1806.390161
< 0.1%
1795.5140491
< 0.1%
1793.9137211
< 0.1%
1792.0622231
< 0.1%

qbelo2_post
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct14648
Distinct (%)100.0%
Missing2162
Missing (%)12.9%
Infinite0
Infinite (%)0.0%
Mean1502.784516
Minimum1149.699743
Maximum1806.22359
Zeros0
Zeros (%)0.0%
Memory size131.5 KiB
2021-04-15T21:45:43.899102image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1149.699743
5-th percentile1332.247478
Q11431.987497
median1505.105619
Q31575.017243
95-th percentile1664.711202
Maximum1806.22359
Range656.5238466
Interquartile range (IQR)143.0297462

Descriptive statistics

Standard deviation101.1324172
Coefficient of variation (CV)0.06729668569
Kurtosis-0.3489191374
Mean1502.784516
Median Absolute Deviation (MAD)71.53567995
Skewness-0.107955453
Sum22012787.59
Variance10227.76581
MonotocityNot monotonic
2021-04-15T21:45:44.019845image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1505.3910561
 
< 0.1%
1616.6244591
 
< 0.1%
1601.5172341
 
< 0.1%
1345.7645461
 
< 0.1%
1353.4716281
 
< 0.1%
1527.3501751
 
< 0.1%
1495.4280431
 
< 0.1%
1579.7978521
 
< 0.1%
1328.8179561
 
< 0.1%
1656.140341
 
< 0.1%
Other values (14638)14638
87.1%
(Missing)2162
 
12.9%
ValueCountFrequency (%)
1149.6997431
< 0.1%
1152.4746511
< 0.1%
1162.5650681
< 0.1%
1178.6190451
< 0.1%
1181.149771
< 0.1%
ValueCountFrequency (%)
1806.223591
< 0.1%
1800.2565921
< 0.1%
1798.8358061
< 0.1%
1789.8741631
< 0.1%
1789.5599061
< 0.1%

score1
Real number (ℝ≥0)

ZEROS

Distinct68
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.61778703
Minimum0
Maximum72
Zeros552
Zeros (%)3.3%
Memory size131.5 KiB
2021-04-15T21:45:44.146938image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q114
median21
Q328
95-th percentile41
Maximum72
Range72
Interquartile range (IQR)14

Descriptive statistics

Standard deviation11.25187244
Coefficient of variation (CV)0.5204914092
Kurtosis0.002351717958
Mean21.61778703
Median Absolute Deviation (MAD)7
Skewness0.3436779558
Sum363395
Variance126.6046333
MonotocityNot monotonic
2021-04-15T21:45:44.271946image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
171125
 
6.7%
201070
 
6.4%
241066
 
6.3%
27895
 
5.3%
14891
 
5.3%
10804
 
4.8%
7778
 
4.6%
21753
 
4.5%
13750
 
4.5%
31694
 
4.1%
Other values (58)7984
47.5%
ValueCountFrequency (%)
0552
3.3%
219
 
0.1%
3345
2.1%
41
 
< 0.1%
510
 
0.1%
ValueCountFrequency (%)
721
< 0.1%
701
< 0.1%
661
< 0.1%
651
< 0.1%
641
< 0.1%

score2
Real number (ℝ≥0)

ZEROS

Distinct63
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.74301011
Minimum0
Maximum73
Zeros967
Zeros (%)5.8%
Memory size131.5 KiB
2021-04-15T21:45:44.401872image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q110
median17
Q326
95-th percentile38
Maximum73
Range73
Interquartile range (IQR)16

Descriptive statistics

Standard deviation10.81629853
Coefficient of variation (CV)0.5770843884
Kurtosis-0.1140888874
Mean18.74301011
Median Absolute Deviation (MAD)7
Skewness0.3608495157
Sum315070
Variance116.9923139
MonotocityNot monotonic
2021-04-15T21:45:44.524614image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
171256
 
7.5%
101081
 
6.4%
71074
 
6.4%
141070
 
6.4%
0967
 
5.8%
24967
 
5.8%
20937
 
5.6%
21844
 
5.0%
13835
 
5.0%
27691
 
4.1%
Other values (53)7088
42.2%
ValueCountFrequency (%)
0967
5.8%
218
 
0.1%
3568
3.4%
510
 
0.1%
6473
2.8%
ValueCountFrequency (%)
731
< 0.1%
661
< 0.1%
651
< 0.1%
632
< 0.1%
621
< 0.1%

Interactions

2021-04-15T21:44:42.311061image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:42.414666image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:42.514574image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:42.609892image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:42.704474image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:42.806960image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:42.906387image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:43.012103image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:43.117632image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:43.225371image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:43.332325image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:43.438913image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:43.542456image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:43.721278image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:43.823040image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:43.925065image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:44.026239image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:44.133145image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:44.242435image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:44.348466image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:44.454040image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:44.552519image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:44.650528image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:44.753538image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:44.859160image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:44.958595image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:45.058147image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:45.162830image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:45.267184image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:45.368479image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:45.469802image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:45.572447image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:45.674939image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:45.776942image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:45.875177image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:46.059564image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:46.155779image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:46.253764image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:46.350793image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:46.452747image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:46.555135image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:46.656548image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:46.756058image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:46.857785image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:46.962605image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:47.061306image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:47.164670image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:47.260181image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:47.356465image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:47.460931image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:47.561030image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:47.661105image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:47.759410image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:47.862044image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:47.965354image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:48.068592image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:48.168519image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:48.266757image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:48.451293image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:48.551284image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:48.650402image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:48.752856image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:48.856114image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:48.958172image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:49.061325image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:49.164521image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:49.267236image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:49.360840image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:49.459563image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:49.555959image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:49.647246image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:49.745792image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:49.839736image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:49.934469image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:50.030579image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:50.127819image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:50.224330image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:50.321530image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:50.416495image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:50.506620image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:50.596883image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:50.778469image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:50.872601image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:50.969136image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:51.065533image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:51.161236image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:51.257611image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:51.353346image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:51.448457image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:51.540596image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:51.636824image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:51.731265image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:51.821079image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:51.917752image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:52.010873image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:52.106022image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:52.201103image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:52.298431image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:52.394960image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:52.490777image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:52.584692image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:52.675231image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:52.765644image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:52.857983image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:52.950571image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:53.134860image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:53.227888image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:53.321236image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:53.417154image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:53.513370image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:53.608854image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:53.710176image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:53.814809image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:53.919065image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:54.018543image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:54.117821image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:54.219396image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:54.320554image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:54.423411image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:54.527648image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:54.630595image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:54.733025image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:54.833845image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:54.930931image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:55.027563image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:55.126197image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:55.225840image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:55.328835image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:55.520043image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:55.621533image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:55.724081image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:55.828524image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:55.933241image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:56.033508image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:56.137193image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:56.237653image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:56.332532image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:56.426512image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:56.527687image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:56.628131image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:56.726949image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:56.829789image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:56.932939image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:57.036770image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:57.137221image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:57.233392image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:57.330347image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:57.429479image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:57.529187image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:57.631631image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:57.733421image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:57.922613image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:58.025648image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:58.128285image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:58.229879image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:58.332108image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:58.430369image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:58.528935image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:58.627144image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:44:58.723782image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
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2021-04-15T21:45:19.563980image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:19.664091image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:19.763641image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:19.862213image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:19.956115image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:20.047884image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:20.141130image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:20.235477image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:20.335148image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:20.433212image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:20.531087image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:20.627126image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:20.724402image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:20.820940image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:20.926253image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:21.031628image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:21.137206image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:21.236422image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:21.334572image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:21.528783image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:21.631013image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:21.730382image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:21.832439image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:21.938462image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:22.044225image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:22.148195image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:22.250199image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:22.348001image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:22.448165image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:22.549409image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:22.651459image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:22.756793image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:22.859647image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:22.963012image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:23.066016image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:23.166676image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:23.271468image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:23.375870image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:23.480507image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:23.579979image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:23.676880image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:23.868402image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:23.971935image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:24.075049image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:24.178832image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:24.283209image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:24.385292image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:24.489835image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:24.593346image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:24.693473image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:24.794189image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:24.894814image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:24.996866image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:25.102185image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:25.205152image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:25.306505image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:25.407667image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:25.511118image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:25.615520image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:25.714994image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:25.816652image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:25.912554image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:26.009540image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:26.197735image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:26.298589image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:26.399218image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:26.501123image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:26.603522image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:26.706555image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:26.808577image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:26.910351image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:27.008382image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:27.104964image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:27.204403image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:27.303708image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:27.404749image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:27.507257image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:27.608148image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:27.710579image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:27.812222image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:27.916630image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:28.017393image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:28.118436image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:28.214535image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:28.311194image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:28.413791image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:28.603468image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:28.705110image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:28.807000image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:28.909916image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:29.012808image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:29.115597image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
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2021-04-15T21:45:29.313170image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:29.410980image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:29.511555image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:29.610885image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:29.713895image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:29.818347image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:29.925871image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:30.032703image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:30.138794image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:30.254385image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:30.365015image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:30.473869image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:30.581478image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:30.685322image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:30.795371image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:30.996322image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:31.104753image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:31.214015image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:31.332023image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:31.492615image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:31.634369image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:31.748531image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:31.860579image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:31.973442image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:32.085030image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:32.187614image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:32.298326image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:32.405113image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:32.509281image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:32.613774image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:32.718289image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:32.821018image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:32.928823image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:33.034497image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:33.133854image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:33.232386image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:33.340140image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:33.531465image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:33.629544image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:33.728171image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:33.831704image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:33.934415image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:34.036320image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:34.136012image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:34.232110image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:34.329998image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:34.435845image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:34.539795image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:34.642325image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:34.748132image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:34.851397image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T21:45:34.950899image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Correlations

2021-04-15T21:45:44.665903image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-04-15T21:45:44.899534image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-04-15T21:45:45.133225image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-04-15T21:45:45.454762image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-04-15T21:45:45.642286image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-04-15T21:45:35.222170image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
A simple visualization of nullity by column.
2021-04-15T21:45:35.924327image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-04-15T21:45:36.349577image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-04-15T21:45:36.651139image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

dateseasonneutralplayoffteam1team2elo1_preelo2_preelo_prob1elo_prob2elo1_postelo2_postqbelo1_preqbelo2_preqb1qb2qb1_value_preqb2_value_preqb1_adjqb2_adjqbelo_prob1qbelo_prob2qb1_game_valueqb2_game_valueqb1_value_postqb2_value_postqbelo1_postqbelo2_postscore1score2
01920-09-2619200NaNRIISTP1503.9471300.0000.8246510.1753491516.1081287.838NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN480
11920-10-0319200NaNCBDPTQ1504.6881300.0000.8252670.1747331516.8031287.885NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN480
21920-10-0319200NaNCHIMUT1368.3331300.0000.6829860.3170141386.5331281.800NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN200
31920-10-0319200NaNRIIMUN1516.1081478.0040.6441710.3558291542.1351451.977NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN450
41920-10-0319200NaNDAYCOL1493.0021504.9080.5758190.4241811515.4341482.475NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN140
51920-10-0319200NaNRCHABU1503.4201300.0000.8242120.1757881510.9341292.486NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN100
61920-10-0319200NaNBFFWBU1478.0041300.0000.8020000.1980001489.7571288.247NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN326
71920-10-0319200NaNAKRWHE1503.4201300.0000.8242120.1757881515.2781288.142NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN430
81920-10-1019200NaNRIIHAM1542.1351444.2590.7186130.2813871559.4051426.990NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN260
91920-10-1019200NaNRCHFTP1510.9341300.0000.8303910.1696091523.6071287.327NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN660

Last rows

dateseasonneutralplayoffteam1team2elo1_preelo2_preelo_prob1elo_prob2elo1_postelo2_postqbelo1_preqbelo2_preqb1qb2qb1_value_preqb2_value_preqb1_adjqb2_adjqbelo_prob1qbelo_prob2qb1_game_valueqb2_game_valueqb1_value_postqb2_value_postqbelo1_postqbelo2_postscore1score2
168002021-01-1020200wTENBAL1599.0765991654.2150040.5141880.4858121577.5958261675.6957771571.6963931651.052726Ryan TannehillLamar Jackson216.955557249.56567313.78883213.1495880.4257140.574286125.204096185.761196207.780411243.1852251554.4002951668.3488241320
168012021-01-1020200wNOCHI1695.6835991500.1184650.8175650.1824351704.0512941491.7507701730.5346381497.160456Drew BreesMitchell Trubisky222.286925164.839331-0.48626518.3244040.8498200.150180267.126273200.161549226.770860168.3715521737.3112961490.383798219
168022021-01-1020200wPITCLE1572.1614421516.9817690.6663690.3336311537.1304861552.0127251578.4457221536.927531Ben RoethlisbergerBaker Mayfield204.930263164.4893128.89891813.3478690.6194430.380557345.501015346.773860218.987338182.7177661546.4289111568.9443433748
168032021-01-1620200dGBLAR1700.2260621620.4985350.6970140.3029861715.6231871605.1014091674.7860931633.700632Aaron RodgersJared Goff266.649955161.67452928.3069864.2188160.7108750.289125366.690032126.791750276.653962158.1862511689.4067921619.0799333218
168042021-01-1620200dBUFBAL1700.5380091675.6957770.6264870.3735131719.9741491656.2596371688.2529861668.348824Josh AllenLamar Jackson289.086698243.18522546.7852859.3018850.6528860.347114131.12018212.093747273.290046220.0760771706.1597661650.442044173
168052021-01-1720200dKCCLE1712.6520901552.0127250.7856470.2143531719.6189211545.0458941711.2170291568.944343Patrick MahomesBaker Mayfield273.850726182.71776610.48129621.7947030.7899270.210073252.952255107.241204271.760879175.1701101718.0322471562.1291252217
168062021-01-1720200dNOTB1704.0512941645.0740080.6712120.3287881669.9390471679.1862551737.3112961624.424406Drew BreesTom Brady226.770860221.7779061.78001622.8598350.7058590.294141-58.497561221.469763198.244018221.7470911700.9462121660.7894892030
168072021-01-2420200cGBTB1715.6231871679.1862550.6419680.3580321691.5061461703.3032961689.4067921660.789489Aaron RodgersTom Brady276.653962221.74709131.89364021.7014360.6291910.370809198.493608121.078453268.837927211.6802271665.8772221684.3190582631
168082021-01-2420200cKCBUF1719.6189211719.9741490.5919720.4080281741.0872791698.5057911718.0322471706.159766Patrick MahomesJosh Allen271.760879273.2900468.91230836.5476950.5356370.464363376.765350241.765435282.261326270.1375851742.9021721681.2898403824
168092021-02-0720201sTBKC1703.3032961741.0872790.5390870.4609131731.8538951712.5366811684.3190581742.902172Tom BradyPatrick Mahomes211.680227282.26132615.58293313.7169160.4662060.533794228.983454-4.844781213.410550253.5507161718.1549411709.066290319